Balancing a simulated inverted pendulum through motor imagery: An EEG-based real-time control paradigm

2012 ◽  
Vol 524 (2) ◽  
pp. 95-100 ◽  
Author(s):  
Jingwei Yue ◽  
Zongtan Zhou ◽  
Jun Jiang ◽  
Yadong Liu ◽  
Dewen Hu
Keyword(s):  
Real Time ◽  
Motor Imagery ◽  
Inverted Pendulum ◽  
Real Time Control ◽  
Time Control ◽  
Control Paradigm

Real-time control and application with self-tuning PID-type fuzzy adaptive controller of an inverted pendulum

2019 ◽  
Vol 46 (1) ◽  
pp. 159-170 ◽  
Author(s):  
Tayfun Abut ◽  
Servet Soyguder
Keyword(s):  
Real Time ◽  
Inverted Pendulum ◽  
Adaptive Controller ◽  
Performance Criteria ◽  
Real Time Control ◽  
Content Type ◽  
Time Control ◽  
Self Tuning ◽  
Fuzzy Adaptive Controller ◽  
Fuzzy Adaptive

PurposeThis paper aims to keep the pendulum on the linear moving car vertically balanced and to bring the car to the equilibrium position with the designed controllers.Design/methodology/approachAs inverted pendulum systems are structurally unstable and nonlinear dynamic systems, they are important mechanisms used in engineering and technological developments to apply control techniques on these systems and to develop control algorithms, thus ensuring that the controllers designed for real-time balancing of these systems have certain performance criteria and the selection of each controller method according to performance criteria in the presence of destructive effects is very helpful in getting information about applying the methods to other systems.FindingsAs a result, the designed controllers are implemented on a real-time and real system, and the performance results of the system are obtained graphically, compared and analyzed.Originality/valueIn this study, motion equations of a linear inverted pendulum system are obtained, and classical and artificial intelligence adaptive control algorithms are designed and implemented for real-time control. Classic proportional-integral-derivative (PID) controller, fuzzy logic controller and PID-type Fuzzy adaptive controller methods are used to control the system. Self-tuning PID-type fuzzy adaptive controller was used first in the literature search and success results have been obtained. In this regard, the authors have the idea that this work is an innovative aspect of real-time with self-tuning PID-type fuzzy adaptive controller.

The Design of Real-Time Control System Based on Single-Inverted Pendulum

2013 ◽  
Vol 850-851 ◽  
pp. 553-556
Author(s):  
Qun Yong Ou
Keyword(s):  
Control System ◽  
Control Problem ◽  
Real Time ◽  
Linear Model ◽  
Inverted Pendulum ◽  
Control Algorithms ◽  
Control Board ◽  
Real Time Control ◽  
Control Software ◽  
Time Control

An inverted pendulum is a classic control problem and is widely used as a benchmark for testing various control algorithms. First, this paper analyse the dynamic and non-linear model of the inverted pendulum, then focus on the real-time control of the inverted pendulum, we developed real-time control software for the single-stage inverted pendulum by using Visual C++ 2010, its mainly operate API functions to control board and implement various control algorithms.

scholarly journals microFE-BCI: An Asynchronous Practical EEG-based Control Paradigm Assisted by Micro-facial-expressions

2021 ◽  
Author(s):  
Zhufeng Lu
Keyword(s):  
Real Time ◽  
Facial Expressions ◽  
Water Volume ◽  
Real Time Control ◽  
Asynchronous Logic ◽  
Time Control ◽  
Control Paradigm ◽  
Significant Difference ◽  
The Stability ◽  
Two Stages

<div><p>In this work, an EEG-based control paradigm assisted by micro-facial-expressions (microFE-BCI) was developed, focusing on the mainstream defect as the insufficiency of real-time capability, asynchronous logic, and robustness. The core algorithm in microFE-BCI contained two stages (asynchronous ‘ON’ detection & microFE-BCI based real-time control) with four steps (obvious non-microFE-EEGs exclusion, interface ‘ON’ detection, microFE-EEGs real-time decoding, and validity judgment). It provided the asynchrounous function, decoded 8 instructions from the latest 100 ms EEGs, and greatly reduced the frequent misoperation. In the offline assessment, microFE-BCI achieved 96.46%±1.07 accuracy for interface ‘ON' detection and 92.68%±1.21 for microFE-EEGs real-time decoding, with the theoretical output timespan less than 200 ms. This microFE-BCI was implemented into a software, and applied to two online manipulations for evaluating the stability and agility. In object-moving with a robotic arm, the averaged IoU was 60.03±11.53%. In water-pouring with a prosthetic Hand, the averaged water volume was 202.5±7.0 ml. During online, microFE-BCI performed no significant difference (P = 0.6521 & P = 0.7931) with commercial control methods (i.e., FlexPendant and Joystick), indicating a similar level of controllability and agility. This study demonstrated the capability of microFE-BCI, enabling a novel solution to the noninvasive BCIs in real-world challenges.</p></div>

scholarly journals microFE-BCI: An Asynchronous Practical EEG-based Control Paradigm Assisted by Micro-facial-expressions

2021 ◽  
Author(s):  
Zhufeng Lu
Keyword(s):  
Real Time ◽  
Facial Expressions ◽  
Water Volume ◽  
Real Time Control ◽  
Asynchronous Logic ◽  
Time Control ◽  
Control Paradigm ◽  
Significant Difference ◽  
The Stability ◽  
Two Stages

<div><p>In this work, an EEG-based control paradigm assisted by micro-facial-expressions (microFE-BCI) was developed, focusing on the mainstream defect as the insufficiency of real-time capability, asynchronous logic, and robustness. The core algorithm in microFE-BCI contained two stages (asynchronous ‘ON’ detection & microFE-BCI based real-time control) with four steps (obvious non-microFE-EEGs exclusion, interface ‘ON’ detection, microFE-EEGs real-time decoding, and validity judgment). It provided the asynchrounous function, decoded 8 instructions from the latest 100 ms EEGs, and greatly reduced the frequent misoperation. In the offline assessment, microFE-BCI achieved 96.46%±1.07 accuracy for interface ‘ON' detection and 92.68%±1.21 for microFE-EEGs real-time decoding, with the theoretical output timespan less than 200 ms. This microFE-BCI was implemented into a software, and applied to two online manipulations for evaluating the stability and agility. In object-moving with a robotic arm, the averaged IoU was 60.03±11.53%. In water-pouring with a prosthetic Hand, the averaged water volume was 202.5±7.0 ml. During online, microFE-BCI performed no significant difference (P = 0.6521 & P = 0.7931) with commercial control methods (i.e., FlexPendant and Joystick), indicating a similar level of controllability and agility. This study demonstrated the capability of microFE-BCI, enabling a novel solution to the noninvasive BCIs in real-world challenges.</p></div>

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